Bayesian generalized biclustering analysis via adaptive structured shrinkage.

ABSTRACT: Biclustering techniques can identify local patterns of a data matrix by clustering feature space and sample space at the same time. Various biclustering methods have been proposed and successfully applied to analysis of gene expression data. While existing biclustering methods have many desirable features, most of them are developed for continuous data and few of them can efficiently handle -omics data of various types, for example, binomial data as in single nucleotide polymorphism data or negative binomial data as in RNA-seq data. In addition, none of existing methods can utilize biological information such as those from functional genomics or proteomics. Recent work has shown that incorporating biological information can improve variable selection and prediction performance in analyses

SUBMITTER: Li Z 

PROVIDER: S-EPMC7307984 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature